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Ines Wilms

Personal Details

First Name:Ines
Middle Name:
Last Name:Wilms
Suffix:
RePEc Short-ID:pwi441
https://www.maastrichtuniversity.nl/i.wilms

Affiliation

Vakgroep Kwantitatieve Economie
School of Business and Economics
Maastricht University

Maastricht, Netherlands
http://www.maastrichtuniversity.nl/web/Faculties/SBE/Theme/Departments/QuantitativeEconomics.htm

: +31 43 388 3834
+31 43 388 4874
P.O. Box 616, 6200 MD Maastricht
RePEc:edi:dqmaanl (more details at EDIRC)

Research output

as
Jump to: Working papers Articles

Working papers

  1. Vanessa Berenguer Rico & Ines Wilms, 2018. "White heteroscedasticty testing after outlier removal," Economics Series Working Papers 853, University of Oxford, Department of Economics.
  2. Luca Barbaglia & Christophe Croux & Ines Wilms, 2017. "Volatility Spillovers and Heavy Tails: A Large t-Vector AutoRegressive Approach," Papers 1708.02073, arXiv.org.
  3. Stéphanie Aerts & Ines Wilms, 2017. "Cellwise robust regularized discriminant analysis," Working Papers Department of Decision Sciences and Information Management 563648, KU Leuven, Faculty of Economics and Business, Department of Decision Sciences and Information Management.
  4. Ines Wilms & Luca Barbaglia & Christophe Croux, 2016. " Multi-class vector autoregressive models for multi-store sales data," Working Papers Department of Decision Sciences and Information Management 540947, KU Leuven, Faculty of Economics and Business, Department of Decision Sciences and Information Management.
  5. Luca Barbaglia & Ines Wilms & Christophe Croux, 2016. "Commodity Dynamics: A Sparse Multi-class Approach," Papers 1604.01224, arXiv.org, revised Oct 2016.
  6. Ines Wilms & Jeroen Rombouts & Christophe Croux, 2016. " Lasso-based forecast combinations for forecasting realized variances," Working Papers Department of Decision Sciences and Information Management 553087, KU Leuven, Faculty of Economics and Business, Department of Decision Sciences and Information Management.
  7. Ines Wilms & Christophe Croux, 2015. " An algorithm for the multivariate group lasso with covariance estimation," Working Papers Department of Decision Sciences and Information Management 516983, KU Leuven, Faculty of Economics and Business, Department of Decision Sciences and Information Management.
  8. Ines Wilms & Sarah Gelper & Christophe Croux, 2015. " The predictive power of the business and bank sentiment of firms: A high-dimensional Granger Causality approach," Working Papers Department of Decision Sciences and Information Management 504661, KU Leuven, Faculty of Economics and Business, Department of Decision Sciences and Information Management.

Articles

  1. I. Wilms & C. Croux, 2018. "An algorithm for the multivariate group lasso with covariance estimation," Journal of Applied Statistics, Taylor & Francis Journals, vol. 45(4), pages 668-681, March.
  2. Ines Wilms & Luca Barbaglia & Christophe Croux, 2018. "Multiclass vector auto†regressive models for multistore sales data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 67(2), pages 435-452, February.
  3. Wilms, Ines & Croux, Christophe, 2016. "Forecasting using sparse cointegration," International Journal of Forecasting, Elsevier, vol. 32(4), pages 1256-1267.
  4. Christophe Croux & Ines Wilms, 2016. "Discussion of ‘Asymptotic Theory of Outlier Detection Algorithms for Linear Time Series Regression Models’," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 43(2), pages 353-356, June.
  5. Barbaglia, Luca & Wilms, Ines & Croux, Christophe, 2016. "Commodity dynamics: A sparse multi-class approach," Energy Economics, Elsevier, vol. 60(C), pages 62-72.
  6. Gelper, Sarah & Wilms, Ines & Croux, Christophe, 2016. "Identifying Demand Effects in a Large Network of Product Categories," Journal of Retailing, Elsevier, vol. 92(1), pages 25-39.
  7. Wilms, Ines & Gelper, Sarah & Croux, Christophe, 2016. "The predictive power of the business and bank sentiment of firms: A high-dimensional Granger Causality approach," European Journal of Operational Research, Elsevier, vol. 254(1), pages 138-147.

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Working papers

  1. Luca Barbaglia & Ines Wilms & Christophe Croux, 2016. "Commodity Dynamics: A Sparse Multi-class Approach," Papers 1604.01224, arXiv.org, revised Oct 2016.

    Cited by:

    1. Jiang, Yonghong & Jiang, Cheng & Nie, He & Mo, Bin, 2019. "The time-varying linkages between global oil market and China's commodity sectors: Evidence from DCC-GJR-GARCH analyses," Energy, Elsevier, vol. 166(C), pages 577-586.

  2. Ines Wilms & Christophe Croux, 2015. " An algorithm for the multivariate group lasso with covariance estimation," Working Papers Department of Decision Sciences and Information Management 516983, KU Leuven, Faculty of Economics and Business, Department of Decision Sciences and Information Management.

    Cited by:

    1. Bai, Ray & Ghosh, Malay, 2018. "High-dimensional multivariate posterior consistency under global–local shrinkage priors," Journal of Multivariate Analysis, Elsevier, vol. 167(C), pages 157-170.

  3. Ines Wilms & Sarah Gelper & Christophe Croux, 2015. " The predictive power of the business and bank sentiment of firms: A high-dimensional Granger Causality approach," Working Papers Department of Decision Sciences and Information Management 504661, KU Leuven, Faculty of Economics and Business, Department of Decision Sciences and Information Management.

    Cited by:

    1. Oscar Claveria & Enric Monte & Salvador Torra, 2018. "“Tracking economic growth by evolving expectations via genetic programming: A two-step approach”," IREA Working Papers 201801, University of Barcelona, Research Institute of Applied Economics, revised Jan 2018.
    2. Oscar Claveria & Enric Monte & Salvador Torra, 2019. "Evolutionary Computation for Macroeconomic Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 53(2), pages 833-849, February.
    3. Oscar Claveria & Enric Monte & Salvador Torra, 2019. "Empirical modelling of survey-based expectations for the design of economic indicators in five European regions," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 46(2), pages 205-227, May.
    4. Alain Hecq & Luca Margaritella & Stephan Smeekes, 2019. "Granger Causality Testing in High-Dimensional VARs: a Post-Double-Selection Procedure," Papers 1902.10991, arXiv.org.

Articles

  1. I. Wilms & C. Croux, 2018. "An algorithm for the multivariate group lasso with covariance estimation," Journal of Applied Statistics, Taylor & Francis Journals, vol. 45(4), pages 668-681, March.
    See citations under working paper version above.
  2. Wilms, Ines & Croux, Christophe, 2016. "Forecasting using sparse cointegration," International Journal of Forecasting, Elsevier, vol. 32(4), pages 1256-1267.

    Cited by:

    1. Liang, Chong & Schienle, Melanie, 2019. "Determination of vector error correction models in high dimensions," Working Paper Series in Economics 124, Karlsruhe Institute of Technology (KIT), Department of Economics and Business Engineering.
    2. Francisco Corona & Graciela González-Farías & Pedro Orraca, 2017. "A dynamic factor model for the Mexican economy: are common trends useful when predicting economic activity?," Latin American Economic Review, Springer;Centro de Investigaciòn y Docencia Económica (CIDE), vol. 26(1), pages 1-35, December.
    3. Constantin ANGHELACHE & Madalina-Gabriela ANGHEL & Tudor SAMSON & Radu STOICA, 2017. "Methods And Techniques For Preparing Forecasts," Romanian Statistical Review Supplement, Romanian Statistical Review, vol. 65(4), pages 26-36, April.
    4. Florin Paul Costel LILEA & Aurelian DIACONU & Radu Titus MARINESCU & Gyorgy BODO, 2017. "Structural Methods Used In Forecasting Studies," Romanian Statistical Review Supplement, Romanian Statistical Review, vol. 65(4), pages 66-74, April.
    5. Ziping Zhao & Daniel P. Palomar, 2017. "Robust Maximum Likelihood Estimation of Sparse Vector Error Correction Model," Papers 1710.05513, arXiv.org.
    6. Smeekes, Stephan & Wijler, Etiënne, 2016. "Macroeconomic Forecasting Using Penalized Regression Methods," Research Memorandum 039, Maastricht University, Graduate School of Business and Economics (GSBE).
    7. Constantin Anghelache & Madalina-Gabriela Anghel & Alina-Georgiana Solomon, 2017. "National Accounts System: Source of Information in Macroeconomic Forecast," International Journal of Academic Research in Accounting, Finance and Management Sciences, Human Resource Management Academic Research Society, International Journal of Academic Research in Accounting, Finance and Management Sciences, vol. 7(2), pages 76-82, April.

  3. Barbaglia, Luca & Wilms, Ines & Croux, Christophe, 2016. "Commodity dynamics: A sparse multi-class approach," Energy Economics, Elsevier, vol. 60(C), pages 62-72.
    See citations under working paper version above.
  4. Gelper, Sarah & Wilms, Ines & Croux, Christophe, 2016. "Identifying Demand Effects in a Large Network of Product Categories," Journal of Retailing, Elsevier, vol. 92(1), pages 25-39.

    Cited by:

    1. Matteo Barigozzi & Marc Hallin, 2017. "A network analysis of the volatility of high dimensional financial series," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 66(3), pages 581-605, April.
    2. Matteo Barigozzi & Marc Hallin, 2015. "Networks, Dynamic Factors, and the Volatility Analysis of High-Dimensional Financial Series," Working Papers ECARES ECARES 2015-34, ULB -- Universite Libre de Bruxelles.
    3. Karray, Salma & Sigue, Simon P., 2016. "Should companies jointly promote their complementary products when they compete in other product categories?," European Journal of Operational Research, Elsevier, vol. 255(2), pages 620-630.
    4. Luca Barbaglia & Christophe Croux & Ines Wilms, 2017. "Volatility Spillovers and Heavy Tails: A Large t-Vector AutoRegressive Approach," Papers 1708.02073, arXiv.org.
    5. Mou, Shandong & Robb, David J. & DeHoratius, Nicole, 2018. "Retail store operations: Literature review and research directions," European Journal of Operational Research, Elsevier, vol. 265(2), pages 399-422.

  5. Wilms, Ines & Gelper, Sarah & Croux, Christophe, 2016. "The predictive power of the business and bank sentiment of firms: A high-dimensional Granger Causality approach," European Journal of Operational Research, Elsevier, vol. 254(1), pages 138-147.
    See citations under working paper version above.

More information

Research fields, statistics, top rankings, if available.

Statistics

Access and download statistics for all items

Co-authorship network on CollEc

NEP Fields

NEP is an announcement service for new working papers, with a weekly report in each of many fields. This author has had 7 papers announced in NEP. These are the fields, ordered by number of announcements, along with their dates. If the author is listed in the directory of specialists for this field, a link is also provided.
  1. NEP-ECM: Econometrics (5) 2016-07-02 2016-11-06 2017-01-15 2017-08-13 2018-06-25. Author is listed
  2. NEP-ETS: Econometric Time Series (3) 2016-07-02 2017-08-13 2017-09-10. Author is listed
  3. NEP-RMG: Risk Management (3) 2016-11-06 2017-08-13 2017-09-10. Author is listed
  4. NEP-AGR: Agricultural Economics (1) 2016-04-16
  5. NEP-COM: Industrial Competition (1) 2016-07-02
  6. NEP-ENE: Energy Economics (1) 2017-09-10
  7. NEP-FOR: Forecasting (1) 2016-11-06
  8. NEP-ORE: Operations Research (1) 2018-06-25

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